Estimating county-level vaccination coverage using small area estimation with the National Immunization Survey-Child.

The National Immunization Survey-Child (NIS-Child) provides annual vaccination coverage estimates in the United States for children aged 19 through 35 months, nationally, for each state, and for select local areas and territories. There is a need for vaccination coverage estimates for smaller geographic areas to support local authority planning and identify counties with potentially low vaccination coverage for possible further intervention. We describe small area estimation methods using 2008-2018 NIS-Child data to generate county-level estimates for children up to two years of age born 2007-2011 and 2012-2016. We applied an empirical best linear unbiased prediction method to combine direct estimates of vaccination coverage with model-based prediction using county-level predictors regarding health and demographic characteristics. We review the predictors commonly selected for the small area models and note multiple predictors related to barriers to vaccination.

Constructing a toolkit to evaluate quality of state and local administrative data.

In the United States, state and local agencies administering government assistance programs have in their administrative data a powerful resource for policy analysis to inform evaluation and guide improvement of their programs. Understanding different aspects of their administrative data quality is critical for agencies to conduct such analyses and to improve their data for future use. However, state and local agencies often lack the resources and training for staff to conduct rigorous evaluations of data quality. We describe our efforts in developing tools that can be used to assess data quality as well as the challenges encountered in constructing these tools. The toolkit focuses on critical dimensions of quality for analyzing an administrative dataset, including checks on data accuracy, the completeness of the records, and the comparability of the data over time and among subgroups of interest. State and local administrative databases often include a longitudinal component which our toolkit also aims to exploit to help evaluate data quality. In addition, we incorporate data visualization to draw attention to sets of records or variables that contain outliers or for which quality may be a concern. While we seek to develop general tools for common data quality analyses, most administrative datasets have particularities that can benefit from a customized analysis building on our toolkit.